Why Singapore’s AI finance race is now about data, not models

Singapore's AI finance race is now driven by data integration and workflow harmonization rather than just deploying models. A Forrester Consulting study on AI adoption in finance across 11 markets, including Singapore, reveals that while Singapore is among the more advanced regions, fragmented systems, inconsistent data, and legacy workflows pose bigger challenges than access to AI models themselves.

Finance leaders in Singapore still see the upside, with 96% expecting AI investment to increase over the next year. The appeal lies in automating tasks like bookkeeping, reporting, forecasting, fraud detection, compliance checks, and scenario modelling. However, the roadblock remains inconsistent data sources across disconnected systems.

Finance teams often work with scattered data from multiple markets, currencies, banks, payment rails, and regulatory regimes. AI can help manage this complexity, but only if it has access to clean, timely, and connected information.

Fragmented data is the primary bottleneck. In Singapore, 64% of finance leaders identified fragmented or inconsistent data as a core barrier to scaling AI, followed by partially digitalized workflows (64%) and siloed data (53%). While 18% of Singapore respondents reported AI running autonomously with minimal human input, this is still far from a fully autonomous finance operation.

Companies in Southeast Asia often expand market by market, adding tools as they go. A payment provider chosen for one country might not work in another, leading to tool fragmentation that becomes a constraint as the business grows. Arnold Chan, General Manager for Asia Pacific at Airwallex, stresses that the biggest obstacle is not access to AI models, but the financial systems beneath them.

"Businesses that connect their financial data, workflows, and operations will be far better positioned to move beyond isolated AI use cases towards more intelligent, autonomous finance," he says.